{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e60e2fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a82ec078",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CliffWalkingEnv:\n",
    "    def __init__(self, ncol, nrow):\n",
    "        self.nrow = nrow\n",
    "        self.ncol = ncol\n",
    "        self.x = 0  # 记录当前智能体位置的横坐标\n",
    "        self.y = self.nrow - 1  # 记录当前智能体位置的纵坐标\n",
    "\n",
    "    def step(self, action):  # 外部调用这个函数来改变当前位置\n",
    "        # 4种动作, change[0]:上, change[1]:下, change[2]:左, change[3]:右。坐标系原点(0,0)\n",
    "        # 定义在左上角\n",
    "        change = [[0, -1], [0, 1], [-1, 0], [1, 0]]\n",
    "        self.x = min(self.ncol - 1, max(0, self.x + change[action][0]))\n",
    "        self.y = min(self.nrow - 1, max(0, self.y + change[action][1]))\n",
    "        next_state = self.y * self.ncol + self.x\n",
    "        reward = -1\n",
    "        done = False\n",
    "        if self.y == self.nrow - 1 and self.x > 0:  # 下一个位置在悬崖或者目标\n",
    "            done = True\n",
    "            if self.x != self.ncol - 1:\n",
    "                reward = -100\n",
    "        return next_state, reward, done\n",
    "\n",
    "    def reset(self):  # 回归初始状态,起点在左上角\n",
    "        self.x = 0\n",
    "        self.y = self.nrow - 1\n",
    "        return self.y * self.ncol + self.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "331c67f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DynaQ:\n",
    "    \"\"\" Dyna-Q算法 \"\"\"\n",
    "    def __init__(self, ncol, nrow, epsilon, alpha, gamma, n_planning, n_action=4):\n",
    "        # 初始化Q(s,a)表格\n",
    "        self.Q_table = np.zeros([nrow * ncol, n_action])\n",
    "        # 动作个数\n",
    "        self.n_action = n_action\n",
    "        # 学习率\n",
    "        self.alpha = alpha\n",
    "        # 折扣因子\n",
    "        self.gamma = gamma\n",
    "        # e-贪婪策略中的参数\n",
    "        self.epsilon = epsilon\n",
    "        \n",
    "        # 执行Q-planning的次数，对应1次Q-learning\n",
    "        self.n_planning = n_planning\n",
    "        # 环境模型\n",
    "        self.model = dict()\n",
    "    \n",
    "    # 选取下一步的操作\n",
    "    def take_action(self, state):\n",
    "        if np.random.random() < self.epsilon:\n",
    "            action = np.random.randint(self.n_action)\n",
    "        else:\n",
    "            action = np.argmax(self.Q_table[state])\n",
    "        return action\n",
    "    \n",
    "    def q_learning(self, s0, a0, r, s1):\n",
    "        td_error = r + self.gamma * self.Q_table[s1].max() - self.Q_table[s0, a0]\n",
    "        self.Q_table[s0, a0] += self.alpha * td_error\n",
    "    \n",
    "    def update(self, s0, a0, r, s1):\n",
    "        self.q_learning(s0, a0, r, s1)\n",
    "        # 将数据添加到模型中\n",
    "        self.model[(s0, a0)] = r, s1\n",
    "        # Q-planning循环\n",
    "        for _ in range(self.n_planning):\n",
    "            # 随机选择曾经遇到过的状态动作对\n",
    "            (s, a), (r, s_) = random.choice(list(self.model.items()))\n",
    "            self.q_learning(s, a, r, s_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df73bf3",
   "metadata": {},
   "source": [
    "### Q - learning 加快训练的原因\n",
    "Q - learning 能够加快训练速度，主要是基于以下几点：\n",
    "- 经验复用：\n",
    "Dyna - Q 算法通过维护一个环境模型 model，将每次与环境交互得到的经验（状态 - 动作对、奖励和下一个状态）存储起来。在 Q - planning 阶段，会从这些存储的经验中随机选取进行多次 Q 值更新，实现了经验的复用，避免了大量重复的环境交互。\n",
    "- 减少环境探索：\n",
    "借助环境模型模拟环境的反馈，算法可以在不与真实环境交互的情况下更新 Q 值。这就减少了在环境中进行探索的次数，进而加快了收敛速度。\n",
    "- 快速适应环境变化：\n",
    "当环境发生变化时，Dyna - Q 算法可以迅速利用已有的经验进行调整。因为它可以在新的经验产生后，立即更新环境模型，并通过 Q - planning 快速更新 Q 值，从而更快地适应新环境。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "116fefa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def DynaQ_CliffWalking(n_planning):\n",
    "    ncol = 12\n",
    "    nrow = 4\n",
    "    env = CliffWalkingEnv(ncol, nrow)\n",
    "    epsilon = 0.01\n",
    "    alpha = 0.1\n",
    "    gamma = 0.9\n",
    "    agent = DynaQ(ncol, nrow, epsilon, alpha, gamma, n_planning)\n",
    "    # 智能体在环境中运行多少条序列\n",
    "    num_episodes = 300\n",
    "\n",
    "    # 记录每一条序列的回报\n",
    "    return_list = []\n",
    "    # 显示10个进度条\n",
    "    for i in range(10):\n",
    "        # tqdm的进度条功能\n",
    "        with tqdm(total=int(num_episodes / 10), desc='Iteration %d' % i) as pbar:\n",
    "            for i_episode in range(int(num_episodes / 10)):\n",
    "                episode_return = 0\n",
    "                state = env.reset()\n",
    "                done = False\n",
    "                while True:\n",
    "                    action = agent.take_action(state)\n",
    "                    next_state, reward, done = env.step(action)\n",
    "                    # 这里回报的计算不进行折扣因子衰减\n",
    "                    episode_return += reward\n",
    "                    '''\n",
    "                    为什么 episode_return 不使用折扣因子？\n",
    "                    在这个代码中，episode_return 的作用是：\n",
    "                        监控训练进度：统计每个 episode 的实际总奖励（无折扣），用于评估智能体的表现。\n",
    "                        不参与算法更新：仅作为评估指标，不影响 Q-table 更新或规划过程。\n",
    "                    例如，在悬崖行走环境中，一个 episode 的原始总奖励为 -1（成功到达终点）或 -100（掉入悬崖），直接累加这些值能直观反映智能体的成功率。\n",
    "                    '''\n",
    "                    agent.update(state, action, reward, next_state)\n",
    "                    state = next_state\n",
    "                    if done:\n",
    "                        break\n",
    "                return_list.append(episode_return)\n",
    "                if (i_episode + 1) % 10 == 0 :\n",
    "                    pbar.set_postfix({'episode': '%d' % (num_episodes / 10 * i + i_episode + 1),\n",
    "                                      'return': '%.3f' % np.mean(return_list[-10:])})\n",
    "                pbar.update(1)\n",
    "    return return_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc05ab0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-planning步数为：0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 30/30 [00:00<00:00, 653.58it/s, episode=30, return=-138.400]\n",
      "Iteration 1: 100%|██████████| 30/30 [00:00<00:00, 1560.01it/s, episode=60, return=-64.100]\n",
      "Iteration 2: 100%|██████████| 30/30 [00:00<00:00, 1845.76it/s, episode=90, return=-46.000]\n",
      "Iteration 3: 100%|██████████| 30/30 [00:00<00:00, 891.70it/s, episode=120, return=-38.000]\n",
      "Iteration 4: 100%|██████████| 30/30 [00:00<00:00, 2638.31it/s, episode=150, return=-28.600]\n",
      "Iteration 5: 100%|██████████| 30/30 [00:00<00:00, 1185.43it/s, episode=180, return=-25.300]\n",
      "Iteration 6: 100%|██████████| 30/30 [00:00<00:00, 2246.11it/s, episode=210, return=-23.600]\n",
      "Iteration 7: 100%|██████████| 30/30 [00:00<00:00, 2104.91it/s, episode=240, return=-20.100]\n",
      "Iteration 8: 100%|██████████| 30/30 [00:00<00:00, 2000.53it/s, episode=270, return=-17.100]\n",
      "Iteration 9: 100%|██████████| 30/30 [00:00<00:00, 1563.27it/s, episode=300, return=-16.500]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-planning步数为：2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 30/30 [00:00<00:00, 461.72it/s, episode=30, return=-53.800] \n",
      "Iteration 1: 100%|██████████| 30/30 [00:00<00:00, 602.72it/s, episode=60, return=-37.100]\n",
      "Iteration 2: 100%|██████████| 30/30 [00:00<00:00, 746.65it/s, episode=90, return=-23.600]\n",
      "Iteration 3: 100%|██████████| 30/30 [00:00<00:00, 1319.71it/s, episode=120, return=-18.500]\n",
      "Iteration 4: 100%|██████████| 30/30 [00:00<00:00, 1179.67it/s, episode=150, return=-16.400]\n",
      "Iteration 5: 100%|██████████| 30/30 [00:00<00:00, 1331.43it/s, episode=180, return=-16.400]\n",
      "Iteration 6: 100%|██████████| 30/30 [00:00<00:00, 1281.80it/s, episode=210, return=-13.400]\n",
      "Iteration 7: 100%|██████████| 30/30 [00:00<00:00, 1203.84it/s, episode=240, return=-13.200]\n",
      "Iteration 8: 100%|██████████| 30/30 [00:00<00:00, 1493.34it/s, episode=270, return=-13.200]\n",
      "Iteration 9: 100%|██████████| 30/30 [00:00<00:00, 846.74it/s, episode=300, return=-13.500]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-planning步数为：20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 30/30 [00:00<00:00, 242.93it/s, episode=30, return=-18.500]\n",
      "Iteration 1: 100%|██████████| 30/30 [00:00<00:00, 361.54it/s, episode=60, return=-13.600]\n",
      "Iteration 2: 100%|██████████| 30/30 [00:00<00:00, 369.17it/s, episode=90, return=-13.000]\n",
      "Iteration 3: 100%|██████████| 30/30 [00:00<00:00, 325.51it/s, episode=120, return=-13.500]\n",
      "Iteration 4: 100%|██████████| 30/30 [00:00<00:00, 416.04it/s, episode=150, return=-13.500]\n",
      "Iteration 5: 100%|██████████| 30/30 [00:00<00:00, 497.55it/s, episode=180, return=-13.000]\n",
      "Iteration 6: 100%|██████████| 30/30 [00:00<00:00, 379.34it/s, episode=210, return=-22.000]\n",
      "Iteration 7: 100%|██████████| 30/30 [00:00<00:00, 458.53it/s, episode=240, return=-23.200]\n",
      "Iteration 8: 100%|██████████| 30/30 [00:00<00:00, 435.09it/s, episode=270, return=-13.000]\n",
      "Iteration 9: 100%|██████████| 30/30 [00:00<00:00, 531.44it/s, episode=300, return=-13.400]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "random.seed(0)\n",
    "n_planning_list = [0, 2, 20]\n",
    "for n_planning in n_planning_list:\n",
    "    print('Q-planning步数为：%d' % n_planning)\n",
    "    time.sleep(0.5)\n",
    "    return_list = DynaQ_CliffWalking(n_planning)\n",
    "    episodes_list = list(range(len(return_list)))\n",
    "    plt.plot(episodes_list, return_list, label=str(n_planning) + 'planning steps')\n",
    "plt.legend()\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('Dyna-Q on Cliff Walking')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
